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In the paper, the mean counts are modeled as Y ~ NB(u = pi * exp(XB + log(w) ) , theta), where pi is the number of UMI's in each cell and theta is the dispersion parameter.
Three questions
Is thi…
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## Before submitting a PR regarding this issue
_This issue is strongly affected by current efforts to update the distribution infrastructure (see #15928). The infrastructure update will improve the…
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Firstly, thanks for the great, smooth and stable package that `ranger` proved to be.
My question/request is, in a sense, an extension to https://github.com/imbs-hl/ranger/issues/136: currently, is …
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@drphilmarshall: I'm wrapping up my little project on hierarchical inference of N(z) using photometric redshift posterior probability distributions, and slowly but surely [the paper](https://github.co…
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# Dependence Entropy | Galen's Blog
I want to share an update in my thinking since asking and answering A formal definition of a “measure of association”. I’ve developed a functional which assigns ho…
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specific application of binned gof test #????
In stats gof applications, I had used chisquare test for continuous distributions by binning the continuous distributions and count observations and pr…
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Hello,
I'm trying to understand how the emission probability is implemented in DXM. I can see the binomial distribution of `i`, which is the number of reads that came from the underlying methylated…
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I am trying to fit various HMM classes (LinearRegressionHMM, or GaussianHMM) to my data but it does not let me pass `num_states=1`. For `num_states > 2`, everything works as expected. I wanted to know…
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In generate.py, it use random.choice with scaled_prediction to predict next sample, i'm confused about why it doesn't use argmax to choose the highest prediction every time?
i have tried it but it…